Creating autonomous robots that can assist humans in situations of daily life is a great challenge for machine learning. While this aim has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences, we have yet to achieve the first step of creating robots that can accomplish a multitude of different tasks, triggered by environmental context or higher level instruction. Despite the wide range of machine learning problems encountered in robotics, the main bottleneck towards this goal has been a lack of interaction between the core robotics and the machine learning communities. To date, many roboticists still discard machine learning approaches as generally inapplicable or inferior to classical, hand-crafted solutions. Similarly, machine learning researchers do not yet acknowledge that robotics can play the same role for machine learning which for instance physics had for mathematics: as a major application as well as a driving force for new ideas, algorithms and approaches.

Robotics challenges can inspire and motivate new Machine Learning research as well as being an interesting field of application of standard ML techniques. Inversely, with the current rise of real, physical humanoid robots in robotics research labs around the globe, the need for machine learning in robotics has grown significantly. Only if machine learning can succeed at making robots fully adaptive, it is likely that we will be able to take real robots out of the research labs into real, human inhabited environments. Among the important problems hidden in these steps are problems which can be understood from the robotics and the machine learning point of view including perceptuo-action coupling, imitation learning, movement decomposition, probabilistic planning problems, motor primitive learning, reinforcement learning, model learning and motor control.